Download Comparative evaluation of the main factors that

NOVATECH 2013
Comparative evaluation of the main factors that
contribute to flood risk in urban drainage areas
Évaluation comparative des principaux facteurs
contribuant au risque d’inondation dans les zones de
drainage urbain
Masaru Morita*
Shibaura Institute of Technology
3-7-5 Toyosu, Koto-ku, Tokyo 135-8548, Japan [email protected]
RÉSUMÉ
Parmi les divers facteurs contribuant au risque d’inondation (ou risque inondatoire), les violents
typhons et les systèmes de drainage des eaux pluviales inadaptés ainsi que la concentration de
population et de biens sont généralement considérés comme des facteurs fondamentaux pour le
drainage urbain. Les changements climatiques devraient également constituer une véritable menace
avec des typhons à la fréquence et violence accrues.
Cette étude présente la méthodologie d'évaluation comparative de l'impact des facteurs de risque
d'inondation à l'aide d'un modèle de prévision du dommage inondatoire (MPDI) basé sur un système
d’information géographique (SIG). Le MDPI calcule les profondeurs des eaux d’inondation via XPSWMM et les dommages financiers dus aux inondations grâce à un modèle d'estimation des
dommages inondatoires pour tous les typhons et conditions de captage donnés. La notion de risque
d’inondation dans ce contexte est définie comme le produit entre les dommages dus aux inondations
et la probabilité de leur occurrence. L'étude montre une structure du risque d’inondation dans un cadre
de gestion de ce risque en milieu urbain.
La méthode d'évaluation du risque est appliquée au bassin du fleuve Zenpukuji à Tokyo, au Japon.
L'étude montre l'évaluation quantitative des changements dans le risque d’inondation en raison de
facteurs de risque d’inondation: l'urbanisation, les projets de contrôle des inondations, les mesures
non structurelles et les changements climatiques via un facteur d'impact du risque d’inondation (FIRI).
ABSTRACT
Among the various factors that contribute to flood risk, heavy storms and inadequate storm drainage
systems and the concentration of population and property have usually been considered to be
fundamental factors affecting urban drainage. Climate change is also a real threat, bringing heavier
and more frequent storms.
This study presents a methodology for comparatively evaluating the impact of the flood risk factors
using a GIS-based flood damage prediction model (FDPM). The FDPM calculates flood inundation
depths using the XP-SWMM routine and monetary flood damages using a flood damage estimation
model for various storms and catchment conditions. The concept of flood risk in this context is defined
as the product of flood damage and the probability of its occurrence. This study produces a flood risk
structure in a framework of urban flood risk management.
The risk assessment method is applied to the Zenpukuji River basin in Tokyo, Japan. The study gives
a quantitative evaluation of the changes in flood risk due to flood risk factors such as urbanization,
flood control projects, non-structural measures, and climate change using a flood risk impact factor
(FRIF).
KEYWORDS
Climate change, Flood inundation modelling, Flood risk assessment, Flood risk impact factor (FRIF)
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C6b - RISQUE INONDATION / FLOOD RISK
1
INTRODUCTION
Many factors contribute to flood risk in urban catchment areas. Hydrological factors cause rapid flood
runoff and flood discharge grows with an increase in impervious areas. Concentration of population
and assets is also important as a social aspect of flood risk. Climate change is now considered an
important factor increasing flood risk, with its increasingly frequent torrential storms (IPCC, 2007;
Patrik Willems et al., 2012). To reduce flood risk, national and local governments have been
implementing structural measures, constructing flood control reservoirs and infiltration and storage
facilities with their budgets available for flood prevention. Significant non-structural measures must
also be employed for flood risk reduction, using such tools as hazard maps and effective forecasting
systems. Flood insurance mitigates flood inundation damage and contributes to relieving flood risk.
For urban flood risk management, these factors that increase or decrease risk should be compared
and evaluated in the decision-making process of urban flood control planning.
There are many different definitions of flood risk. Crichton (1999) gives the simplest, a comprehensive
definition using the “Crichton Risk Triangle” that consists of hazard, vulnerability, and exposure. Some
researchers adopt the narrow definition of flood risk as the probability of failure during a flood event
(e.g., National Research Council, 2000). Some other researchers strongly emphasize the mental
aspect of flood risk (Baan, 2005). Samuels (2013) broadly discusses flood risk management and gives
the definition of flood risk as an evaluation of the combination of the probability of flooding and the
adverse consequences that ensures. Klijn et al. (2008) shows the standard definition of flood risk as
the ‘product’ of the probability of floods and their consequences. This definition enables us to evaluate
flood risk on a monetary basis though it is by no means new. Accordingly, flood risk assessment
focuses on the frequency and severity of flooding events (Davis, 2003; Morita, 2011). Whatever the
definition of flood risk, quantifying flood risk provides a basis for evaluating flood prevention measures
and making appropriate decisions in flood control management.
Climate change will likely affect flood risk increases, especially future storms’ frequency and severity.
This can be forecast in changes of rainfall intensity-duration-frequency relationships used in urban
drainage planning (Nguyen et al., 2007; Patrick, W. et al., 2012). Such a projection, formulated as a
return period shift (RPS) method, has been applied to evaluating flood risk increases due to global
warming (Morita, 2013).
This study presents a methodology to comparatively evaluate the impact of flood risk factors using a
GIS-based flood damage prediction model (FDPM). The FDPM calculates flood inundation depths
using the XP-SWMM program and estimates monetary flood damages with a flood damage estimation
model for any given storm and its catchment conditions. Flood risk in this context is defined as the
product of flood damage and the probability of its occurrence.
The risk assessment method is applied to the Zenpukuji River basin in Tokyo, Japan. The study
reports a quantitative evaluation of the changes in flood risk due to the following flood risk factors:
urbanization, flood control projects, non-structural measures and climate change using a flood risk
impact factor (FRIF). It provides a flood risk structure in a framework of urban flood risk management.
2
METHODS
This study’s methodology consists of three stages: FDPM simulation, flood risk analysis, and flood risk
assessment. This methodology uses the results of FDPM simulations and provides a basis for flood
risk assessment to calculate flood risk costs and flood risk impact factors.
2.1
FDPM simulation
FDPM simulations provide three curves: an inundation characteristic curve, a damage characteristic
curve, and an inundation-damage characteristic curve (Figure 1).
2.1.1 Flood damage prediction model
FDPM, a GIS-based flood damage prediction model, consists of two parts, Model 1 and Model 2.
Model 1 calculates two-dimensional inundation depths for input hyetographs. Model 2 estimates
monetary inundation damages for any given inundation depths (Figure 1).
For Model 1, XP-SWMM, a storm water modeling software package for urban drainage (Phillip et al.
2005), is used to calculate the inundation depths on a square grid for a given hyetograph. Other flood
inundation simulation models can also be used for Model 1.
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NOVATECH 2013
Model 2 estimates the monetary cost of inundation damage, including direct and indirect damages, as
a function of inundation depth. To calculate direct
damages, an asset valuation of each item is
Design Storm (T-year Return Period)
basically multiplied by the damage rate determined
from the depth-damage rate curve. Indirect
Intensity-duration-frequency curves
damage is calculated using the number of days
Alternating Block Method
business is interrupted for each place of business.
2.1.2
Three curves for inundation
damage characteristics
and
Design Storm Hyetograph
FDPM
The inundation depths for any given storm are
calculated in two-dimensions using Model 1. The
inundation characteristic curve shows the
relationship between inundation depth and the
flooded area for different return period storms. The
curves are shown for only one return period in
Figure 1(a). The integral of the curve equals the
total area inundated in the catchment for the FDPM
calculation.
The monetary costs of inundation are estimated for
any given inundation depth using Model 2 as
shown in Figure 1. The damage characteristic
curve gives the relationship between the inundation
depth and monetary inundation damage per unit
area averaged for the catchment (Figure 1(b)).
Catchments actually have various land uses and
the population and assets are not distributed
uniformly. This curve can be used for catchments
that are relatively spatially homogeneous.
Model 2
Inundation depth
(XP-SWMM)
Depth-damage
rate curve
Effective rainfall
Sewer flow
Damage Rate
GIS
Asset valuation
(Channel flow)
Inundation depth
Inundation damage
1000
Urbanization
Climate change
Inundation area (L**2/L)
For the inundation characteristic curve, the solid
line shows the inundation for present catchment
conditions as in Figure 1(a). Urban development
and global warming shift the present inundation
upward as shown by the dashed lines. Conversely,
flood control projects decrease the inundation as
shown by the dotted lines.
Model 1
800
600
Present catchment condition
400
Flood control project
200
Urbanization concentrates population and assets
and thus increases the potential for damage in
0
0
0.5
Inundation1 depth (L)1.5
2
(a) Inundation characteristic curve.
700
Concentration of population
and assets
Urbanization etc.
600
Inundation damage ($ /L)
Damage per unit area ($ /L**2)
20
15
500
Concentration of
population and
assets
400
10
300
Present catchment
condition
200
5
Present catchment
condition
100
0
0
0
0.5
1
1.5
Inundation
depth
(L)
2
(b) Damage characteristic curve.
2.5
0
0.5
1
1.5
Inundation depth (L)
2
2.5
(c) Inundation-damage characteristic curve.
Figure 1. Flood damage prediction model and inundation-damage characteristics.
urban catchments. This means urbanization or the concentration of assets shift the damage
characteristic curve upwards as shown by the dashed line in Figure 1(b). Conversely, the removal of
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C6b - RISQUE INONDATION / FLOOD RISK
household articles before inundation could reduce the inundation damage as a non-structural measure.
Damage Potential
The monetary inundation damage is calculated by multiplying a value from the inundation
characteristic curve by the appropriate value from the damage characteristic curve. The curve thus
obtained is termed the inundation-damage characteristic curve which shows the relationship between
inundation depth and the monetary damage over the
whole catchment for one return period storm (Figure
1.2
1(c)). The integral of the curves for different return
Urbanization
periods gives the monetary damages for storms having
1
Climate change
different return periods. The results are used to devise
etc.
the damage potential curve described below.
0.8
The inundation and damage characteristics for a
catchment are described by the three curves. These
curves reflect the conditions in the catchment.
2.2
Flood risk analysis
0.6
0.4
The flood risk analysis uses three curves: a storm
probability curve, a damage potential curve, and an
annual risk density curve (Figure 2). In the flood risk
analysis, multiplying the storm probability curve by the
inundation damage potential curve produces the annual
risk density curve. The risk density curve gives useful
information on flood risk characteristics and risk cost.
0
10
(a ) Damage potential curve.
1
0.8
The results of integrating the inundation-damage
characteristic curves for different return periods are
plotted as a damage potential curve, which shows the
relationship between the design storm return period
and flood damage as in Figure 2(a). The damage
potential curve can also be calculated directly from the
FDPM using a GIS assets database for cases where
the land use in and asset distribution in the catchment
are not homogeneous (Morita, 2011).
2.2.2
Interconnected structure of flood risk
f(T) = 1/T2
0.6
0.4
0.2
0
The design storms used in the FDPM simulations are
specified by their return periods. The return period T
and cumulative probability P are related by the equation
P = 1 – 1/T. Thus, the probability density is f(T) = 1/T2,
as given in Figure 2(b).
The three curves describe the characteristics of flood
risk for a catchment.
100
Storm Level (Return Period)
Three curves for flood risk analysis
1
10
Storm Level (Return
Period)
(b) Storm probability curve.
8
7
Annual Risk Density
In our study, flood risk is defined as the product of flood
inundation damage and the probability of its occurrence.
Thus, flood inundation risk is quantified using the
estimates of monetary damage caused by the design
storm and that storm’s probability of occurring. The
annual risk density curve, which has a peak in the
middle as shown in Figure 2(c), is thus obtained by
multiplying the increasing damage potential curve and
the decreasing storm probability curve.
Flood
control
project
0.2
Probability Density
2.2.1
Present
catchment
condition
6
Urbanization,
climate change
etc.
5
4
3
Flood control
project
Present
catchment
conditions
2
1
0
10
100
Storm Level (Return Period)
FDPM simulations for different return period storms first
(c) Annual Risk density curve.
provide the inundation and damage characteristic
curves. The two characteristic curves are both linked to
Figure 2. Flood risk analysis and
the inundation-damage characteristic curve. The
its three associated curves.
integrals of the inundation-damage characteristic
curves for different return periods are transformed into the damage potential curve. The product of the
damage potential curve and the storm probability curve produces the annual risk density curve.
4
NOVATECH 2013
The three curves obtained by the FDPM simulations shift with changes in catchment conditions.
Similarly, the damage potential curve shifts upwards due to urban development, global climate change,
and increased assets, and downwards with the implementation of flood control projects as shown in
Figure 2(a). Changes in the damage potential curve are, in turn, linked with the risk density curve as
shown in Figure 2(c).
The six curves are, as stated above, interconnected and reflect the effects of urbanization, climate
change, flood control projects and other positive or negative factors.
2.3
Flood risk assessment
2.3.1 Risk cost
In this study, we take flood risk cost to be the annually averaged monetary expenditure over time for
flood inundation damages. The risk cost depends on the annual risk density curve and is obtained by
integrating the risk density curve with respect to return period.
The flood risk cost for present catchment conditions decreases with the implementation of flood control
projects and increases with global climate change because heavy storms are expected to become
more frequent due to global warming.
The risk costs for present catchment conditions and future conditions are RC0 and RC, respectively.
The change in flood risk cost should be the difference between the two, RC = RC – RC0.
2.3.2 Flood risk impact factor (FRIF)
Urban river basins are exposed to various flood risks such as increasing impervious area caused by
urban development and the effects of global climate change. Conversely, flood control projects are
effective in reducing the flood risk in urban catchments.
The factors that affect risk cost can be evaluated on a single scale of one simple index, FRIF. This is
computed as FRIF = (RC – RC0)/RC0. A positive FRIF value indicates increased flood risk, whereas a
negative one indicates reduced flood risk. The magnitude of the impact factor indicates the importance
of the risk-increasing or risk-decreasing effects of changing conditions in urban catchment areas.
3
APPLICATION OF FLOOD RISK ASSESSMENT
This risk assessment method, based on FDPM simulations, was applied to the Zenpukuji River basin
in the Tokyo Metropolis. The Zenpukuji River basin, which has an area of 18.3 km2, is characterized
by a high population density and numerous assets. Repeated flood inundation disasters prompted the
Tokyo Metropolitan Government (TMG) to construct an underground flood control reservoir under
Loop 7 Road in 2006. The reservoir has a capacity of 540000 m3, receiving floodwater from the
Zenpukuji, Myousyouji and Kanda rivers. Figure 3 shows the outline of the Zenpukuji River basin and
the flood control reservoir called the Loop 7 Reservoir.
3.1
FDPM simulation
Flood inundation depths and damages were
calculated using the FDPM Model 1 and
Myousyouji River
Model 2 for the present catchment
R0
R1
Kanda River
Zenpukuji River
conditions. A set of design hyetographs
having different return periods was used for
FDPM simulations. Each hyetograph input to
Kanda River
R0
the FDPM is created from the IntensityR0
Duration-Frequency (IDF) relationship for its
corresponding return period using the
0 1 2 3 km
alternating block method (e.g., Ven Te Chow
et al., 1988). The IDF curves for the study
Figure 3. Outline of Zenpukuji River basin. R0:
were determined by the Gumbel distribution
Loop 7 Reservoir ; R1: assumed reservoir
and have been adopted by the TMG. Figure
under Loop 8 Road.
4 shows some examples of the IDF curves
for different return periods for present
conditions. Design storms for the simulations have return periods of 1, 2, 3, 4, 5, 15, 30, 50, 70, 100,
150, 200, 300, and 500 years.
5
250
C6b - RISQUE INONDATION / FLOOD RISK
Rain intensity (mm/hr)
T = 3 yr
For Model 1, we used XP-SWMM to calculate
200
inundation depths in two-dimensions on a 50 m
T = 5 yr
square grid for the design storms for a given
T = 10 yr
hyetograph. The inland flows from the sewer
150
pipes and overflows from the river channel
T = 30 yr
were both calculated spatially and temporarily
for the catchment. The time increment t was
T = 100 yr
100
set to be 1.0 s for calculation stability. The
inundating water was assumed to flow on the
natural surface according to the 50 m digital
50
elevation model (DEM). The water pathways
and detailed land use were taken into account
by adjusting their roughness coefficients. An
0
20
40
60
80 100 120 140 160 180
example of flood inundation calculations is
shown with GIS data superposed in Figure 5
Duration (min)
for a 15-year return period storm under present
catchment conditions. The calculated results of Figure 4. Variation of intensity-duration-frequency
inundation depths are described as an relationships and return period.
inundation characteristic curve for each return
period storm as shown in Figure 1(a) for the Zenpukuji River basin.
The monetary damages are calculated by Model 2 as a function of the inundation depth using a GIS
database available from the TMG. The effect of water velocity on the damage can be negligible. The
results are expressed as a damage characteristic curve for the catchment as shown in Figure 1(b).
Multiplying the damage characteristic curve by the inundation characteristic curves for the different
return period storms gives estimated monetary inundation damages to provide a damage potential
curve used in the following flood risk analysis.
Depth (m)
Figure 5. Calculated inundation depths for a 15-year return period storm in the
present catchment with GIS data superposed for the onset area in Figure 3.
3.2
Changes in present catchment conditions
Various factors increase or decrease the present flood risk in urban areas. Flood control projects such
as the construction of flood control reservoirs definitely reduce flooding in the Zenpukuji River basin. In
the study, the FDPM simulations deal with one case that assumes that a flood control project (another
flood control reservoir under Loop 8 Road) will be built (R1 in Figure 3).
Concentration of assets in a catchment increases the cost of flood inundation damages and
exacerbates the flood risk. In the simulations, assets are also assumed to increase by 30%. For
6
NOVATECH 2013
example, economic growth of 1% per year is expected to produce 30% growth in a quarter of century.
This would shift the damage characteristic curve shown in Figure 1(b) upward.
3.3
Change in storm characteristics due to global climate change
Climate change is a significant factor in increased flood risk. Risk assessment needs not only the
current design hyetographs but also predicted hyetographs reflecting the effect of global warming.
3.3.1
Changes in IDF relationships
4
Return period (year) after climate change
Return period (year) before climate change
An IDF relationship should be estimated to evaluate the effect off global climate change on flood risk.
Nguyen (2007) presented a detailed spatial and temporal downscaling method based on global
climate models (GCMs) and estimates the resulting variation in IDF curves. However, few studies deal
directly with the change in magnitude and frequency of heavy storms in the Tokyo area. Only two
studies have predicted changes based on General Circulation Models (GCMs): The National Institute
for Land and Infrastructure Management (NILIM, 2008) and Oki (2006) of the Institute of Industrial
Science (IIS). Oki produced the relationship between return periods before and after climate change
shown in Figure 6.
500
500
3.3.2 Return
period
shift
(RPS)
method
200
200
Morita (2012) presented a simple way, the
100
100
Return Period Shift (RPS) method, to deal
with changes in return period due to global
climate change by shifting return periods in
30
30
the damage potential curve. In this study,
the return period shift method is further
10
10
simplified to obtain an inundation
characteristic curve after climate change as
5
5
shown in Figure 1(a). For example, a return
period of 30 years before climate change
corresponds to a 15-year return period after
1
10
the change as shown in Figure 6. The
0.2
0.4
0.6
0.8
1
calculated inundation characteristic curve
for a return period of 30 years before Figure 6. Return periods before and after climate
climate change, therefore, can be change (Oki, 2006).
interpreted as that of a 15-year return
period after climate change. The inundation characteristic curve for a 15-year return period after
climate change thus shifts to the 30-year return period. Accordingly, all of the inundation characteristic
curves for various return periods shift to the new corresponding return periods.
RESULTS AND DISCUSSION
The results of the FDPM simulations produce the three curves for the inundation and damage
characteristics of the catchment shown in Figure 1. Those three curves were followed by the three
curves used to analyze flood risk: the damage potential curve, storm probability curve, and annual risk
density curve. Finally, we used the risk assessment method to compute the flood risk costs and FRIFs
to compare and evaluate the main factors contributing to flood risk in the catchment.
4.1
Inundation and damage characteristics
The inundation characteristic curves were calculated for different return periods. Figure 7 shows the
inundation characteristic curves of some return periods for the present catchment conditions. The
inundation depth and area are larger for higher return period storms. The curves for 15-, 50-, and 100year return periods have peaks at 0.9, 1.1, and 1.3 m of inundation, respectively. The peaks of
inundation depth move to the right from the 15-year to the 100-year return period. The inundated
areas are almost the same, from 100 to 150 ha/m for these peaks. This means there should be a large
ponded area in the catchment.
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C6b - RISQUE INONDATION / FLOOD RISK
The inundation characteristic curve for the present changes if flood control measures are taken or if
global climate change occurs. Figure 8 shows the inundation characteristic curves for a 15-year return
period storm for three cases: present catchment conditions, construction of another flood control
reservoir under Loop 8 Road, and global climate change expressed by the curve for a 30-year return
period storm. Naturally, shallowly inundated areas are larger than those covered by deeper water.
Climate change causes heavier storms and thus increases the inundation area.
500
400
Area of inundation (ha/m)
Area of inundation (ha/m)
500
3-year
15-year
300
50-year
100-year
200
100
0
0.5
1
1.5
2
2.5
Inundation depth (m)
400
Present
300
Climate change
Flood control project
200
100
0
3
Figure 7. Inundation characteristic curves of
different return period storms for present
catchment conditions.
0.5
1
1.5
2
2.5
Inundation depth (m)
3
Figure 8. Inundation characteristic curves
for present catchment conditions, a flood
control project, and climate change.
The damage characteristics reflect the asset valuation of the catchment. Figure 9 shows the damage
characteristic curves for present conditions and for a 30% increase in assets. The larger the
inundation depth, the more inundation damage occurs.
20
15
10
Present
5
Asset increase 1.3
0
0.5
1
1.5
2
2.5
3
Inundation depth (m)
Figure 9. Damage characteristic curves for
present catchment conditions, increasing
assets by 30%.
8
Inundation damage (US $ millions / m)
Damage per unit area (US $ millions / ha)
Multiplying the inundation characteristic curve by the damage characteristic curve produces the
inundation-damage characteristic curve that relates inundation damage to inundation depth as shown
in Figure 10. We can easily see that the most damage occurs at an inundation depth of about 1.0 m.
The floor level of buildings in the catchment seems to be about 0.5 m. This means that most of the
damage is due to flooded household articles in private houses and the loss of depreciable and
inventory assets in business buildings.
1200
Climate change
Asset increse 1.3
Present
Flood control prolect
1000
800
600
400
200
0
0.5
1
1.5
2
Inundation depth (m)
2.5
3
Figure 10. Inundation-damage characteristic
curves for present catchment conditions, a
flood control project, and climate change.
NOVATECH 2013
4.2
Damage potential and annual risk density
The damage potential curve relates storm levels expressed in return periods and the monetary cost of
flood damage. It is computed by integrating the inundation-damage characteristic curve for each return
period, and is plotted for the different return periods. The damage potential curves are shown in Figure
11 for the four cases in Figure 10. The damage potential curves were also calculated for combinations
of such as assuming climate change and the construction of a flood control project. The monetary
flood damage are divided by the average annual Budget for Flood Prevention (BFP) of the Tokyo
Metropolitan Government (approximately 500 million US$) to express them in non-dimensional form.
Multiplying the damage potential curves by the storm probability curve produces the annual risk
density curves for the four cases as shown in Figure 13. The risk density curves have a peak at 2
years. The risk density rises due to increased assets and climate change and drops due to the effects
of the flood control project. The risk density curves were also computed for the same combined cases
as the damage potential curves.
1
7
6
5
0.8
0.6
4
0.4
3
2
probability
density
1
0
1
0.2
0
10
100 200
Storm level (return period: year)
Figure 11. Damage potential curves for
present catchment conditions, a flood control
project, increased assets, and climate change.
4.3
0.3
Annual risk density (BFP/year)
Climate change
Asset increase 1.3
Present
Flood control project
Probability density (1/year)
Damage potential (BFP)
8
0.25
Climate change
Asset increse 1.3
Present
Flood control project
0.2
0.15
0.1
0.05
0
1
10
100 200
Storm level (return period: year)
Figure 12. Annual risk density curves for
present catchment conditions, a flood control
project, increased assets, and climate change.
Risk cost and risk cost change
Flood risk costs were calculated by integrating the annual risk density curves for the four cases:
present catchment conditions, climate change (C.C.), a flood control project (F.C.P.), and asset
increase (A.I.). Risk costs of combinations of these cases were also computed. The values of the risk
costs are read on the x-axis in Figure 13. The risk cost grows as assets increase, but it decreases
when flood control projects are implemented. The risk cost for the present catchment conditions due to
global climate change is estimated to increase by approximately 70% and, remarkably, grow by over
110% when the effect of increased assets is included.
4.4
Flood risk impact factors
We made a comparative evaluation of the effects of the main factors that contribute to flood risk,
computing the flood risk impact factors (FRIFs) for the four cases and their combinations. Flood risk
impact factors are easily computed using the calculated risk costs in Figure 13. The values of the
FRIFs are read on the y-axis in Figure 13.
As a single factor, climate change has the largest flood risk impact factor, 0.70, showing it to have the
largest effect. A 30% increase in assets has a positive impact factor of 0.29. Conversely, constructing
the flood control reservoir on the Loop 8 Road has a negative effect with an impact factor of -0.25.
Combining increased assets and the flood control project balance their negative and positive effects,
yielding a risk impact factor of almost zero. From the inundation-damage characteristic curve shown in
Figure 10, most of the damage is due to flooded household articles in private houses and the loss of
depreciable and inventory assets in business buildings, as mentioned earlier. If people have sufficient
warning and can remove some of their possessions from harm’s way, increased assets become less
9
C6b - RISQUE INONDATION / FLOOD RISK
5
CONCLUDING REMARKS
The objective of this study was to present a
risk assessment method for making
comparative evaluations of the main factors
contributing to flood risk in urban drainage
areas. The important results are as follows:
(1) We have developed a risk assessment
method incorporating a GIS-based FDPM
using the XP-SWMM routine that can
evaluate factors that increase and decrease
the risk of urban flooding to serve as a basis
for urban river management.
1.5
Flood risk impact factor (FRIF)
effective and the FRIF of the combination is
negative. As a result, the flood control project
would be more effective combined with this
non-structural measure.
C. C. + A. I.
No climate change
Climate change
Present
1
C. C.
0.5
A. I. + F. C. P.
0
0.4
0.6
0.8
C. C. + F. C. P
+ A. I.
1
1.2 1.4
Risk cost (BFP)
1.6
1.8
+ F. C. P
-0.5
Figure 13. Flood risk impact factors and risk
costs for present catchment conditions, a flood
control project, asset increase, and climate
change. A.I. : asset increase; C.C.: climate
change; F.C.P.: flood control project.
(2) The risk assessment method was
employed to estimate the reduction in flood
risk provided by a flood control project and the increased risk due to asset increases and global
climate change for the Zenpukuji River basin in Tokyo.
(3) We introduced the FRIF as an index that can be used to evaluate the effectiveness of various
factors that could increase or reduce flood inundation risk. Risk impact factors calculated from FDPMs
may play an important role in urban flood control planning and decision-making processes in the future.
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